Method and apparatus for noise reduction particularly in hearing aids

ABSTRACT

This invention describes a practical application of noise reduction in hearing aids. Although listening in noisy conditions is difficult for persons with normal hearing, hearing impaired individuals are at a considerable further disadvantage. Under light noise conditions, conventional hearing aids amplifying the input signal sufficiently to overcome the hearing loss. For a typical sloping hearing loss where there is a loss in high frequency hearing sensitivity, the amount of boost (or gain) rises with frequency. Most frequently, the loss in sensitivity is only for low-level signals; high level signals are affective minimally or not at all. A compression hearing aid is able to compensate by automatically lowering the gain as the input signal level rises. This compression action is usually compromised under noisy conditions. In general, hearing aids are of lesser benefit under noisy conditions since both noise and speech are boosted together when what is really required is a reduction of the noise relative to the speech. A noise reduction algorithm with the dual purpose of enhancing speech relative to noise and also providing a relatively clean signal for the compression circuitry is described.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims benefit from U.S. provisional application Ser.No. 60/041,991 filed on Apr. 16, 1997.

FIELD OF THE INVENTION

This invention relates to noise reduction in audio or other signals andmore particularly relates to noise reduction in digital hearing aids.

BACKGROUND OF THE INVENTION

Under noisy conditions, hearing impaired persons are severelydisadvantaged compared to those with normal hearing. As a result ofreduced cochlea processing, hearing impaired persons are typically muchless able to distinguish between meaningful speech and competing soundsources (i.e., noise). The increased attention necessary forunderstanding of speech quickly leads to listener fatigue.Unfortunately, conventional hearing aids do little to aid this problemsince both speech and noise are boosted by the same amount.

Compression algorithms used in some hearing aids boost low level signalsto a greater extent than high level signals. This works well with lownoise signals by raising low level speech cues to audibility. At highnoise levels, compression performs only modestly since the action of thecompressor is unduly influenced by the noise and merely boosts the noisefloor. For persons that frequently work in high ambient soundenvironments, this can lead to unacceptable results.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a two-fold approach to sound qualityimprovement under high noise situations and its practical implementationin a hearing aid. In one aspect, the present invention removes noisefrom the input signal and controls a compression stage with a cleanersignal, compared to the use of the original noisy input signal forcontrolling compression as is done in the prior art. The signal foramplification is, optionally, processed with a different noise reductionalgorithm. Under certain circumstances, it may be desirable to use thesame noise reduced signal for application and compression control inwhich case the two noise reduction blocks merge. In another instance, itmay be desirable to alter or eliminate the noise reduction in the upperpath.

Clearly, noise reduction is not suitable for all listening situations.Any situation where a desired signal could be confused with noise isproblematic. Typically these situations involve non-speech signals suchas music. A remote control or hearing aid control will usually beprovided for enabling or disabling noise reduction.

The present invention is based on the realization that, what isrequired, is a technique for boosting speech or other desired soundsource, while not boosting noise, or at least reducing the amount ofboost given to noise.

In accordance with a first aspect of the present invention, there isprovided a method of reducing noise in a signal containing speech andnoise related to each other by a signal to noise ratio, the methodcomprising the steps:

(1) detecting the presence and absence of speech;

(2) in the absence of speech, determining a noise magnitude spectralestimate (|{circumflex over (N)}(f)|);

(3) in the presence of speech, comparing the magnitude spectrum of theinput signal (|X(f)|) to the noise magnitude spectral estimate(|{circumflex over (N)}(f)|);

(4) calculating an attenuation function (H(f)) from the magnitudespectrum of the input signal (|X(f)|) and the noise magnitude spectralestimate (|{circumflex over (N)}(f)|), the attenuation function (H(f))being dependent on the signal to noise ratio; and,

(5) modifying the input signal by the attenuation function (H(f)), togenerate a noise reduced signal wherein there is no substantialmodification to the input signal for very low and for very high signalto noise ratios.

Preferably, the method further comprises the steps of:

(6) supplying the input signal to an amplification unit;

(7) providing the noise reduced signal to a compression circuit whichgenerates a control signal for the amplification unit; and,

(8) controlling the amplification unit with the control signal to modifythe input signal to generate an output signal with compression andreduced noise. Advantageously, step (6) comprises subjecting the inputsignal to a main noise reduction algorithm to generate a main noisereduced signal and providing the main noise reduced signal to theamplification unit.

Furthermore, in one embodiment, step (6) comprises applying the steps(1) to (5) to the input signal prior to supplying the input signal tothe amplification unit. Accordingly, the input signal may be subjectedto a main noise reduction algorithm to generate a modified input signalwhich is supplied to the amplification unit. The auxiliary noisereduction algorithm may comprise the same noise reduction method as themain noise reduction algorithm. Alternatively, the auxiliary noisereduction algorithm may be different from the noise reduction method inthe main noise reduction algorithm.

Conveniently, the square of the speech magnitude spectral estimate(↑Ŝ(f)|) may be determined by subtracting the square of the noisemagnitude spectral estimate (|{circumflex over (N)}(f)|) from the squareof the magnitude spectrum of the input signal (|X(f)|). In a preferredembodiment, the attenuation factor is a function of frequency and iscalculated in accordance with the following equation:${H(f)} = \left\lbrack \frac{{{X(f)}}^{2} - {\beta{{\hat{N}(f)}}^{2}}}{{{X(f)}}^{2}} \right\rbrack^{\alpha}$where f denotes frequency, H(f) is the attenuation function, |X(f)| isthe magnitude spectrum of the input audio signal; (|{circumflex over(N)}(f)|) is the noise magnitude spectral estimate, β is anoversubtraction factor and α is an attenuation rule, wherein α and β areselected to give a desired attenuation function. The oversubtractionfactor β is, preferably, varied as a function of the signal to noiseratio, with β being zero for high and low signal to noise ratios andwith β being increased as the signal to noise ratio increases above zeroto maximum value at a predetermined signal to noise ratio and for highersignal to noise ratios β decreases to zero at a second predeterminedsignal to noise ratio greater than the first predetermined signal tonoise ratio.

Advantageously, the oversubtraction factor β is divided by a preemphasisfunction of frequency P(f) to give a modified oversubtraction factor{circumflex over (β)}(f), the preemphasis function being such as toreduce {circumflex over (β)}(f) at high frequencies, to reduceattenuation at high frequencies.

Preferably, the rate of the attenuation factor is controlled to preventabrupt and rapid changes in the attenuation factor, and it preferably iscalculated in accordance with the following equation where G_(n)(f) isthe smoothed attenuation function at the n'th time frame:G _(n)(f)=(1−γ)H(f)+γG _(n-1)(f)

The oversubtraction factor β can be a function of perceptual distortion.

The method can include remotely turning noise suppression on and off.The method can include automatically disabling noise reduction in thepresence of very light noise or extremely adverse environments.

Another aspect of the present invention provides for a method ofdetecting the presence or the absence of speech in an audio signal, themethod comprising taking a block of the audio signal and performing anauto-correlation on that block to form a correlated signal; and checkingthe correlated signal for the presence of a periodic signal having apitch corresponding to that for speech.

In a further aspect the present invention provides an apparatus forreducing noise in an input signal, the apparatus including an input forreceiving the input signal. The apparatus comprises a compressioncircuit for receiving a compression control signal and generating anamplification control signal in response, and an amplification unit forreceiving the input signal and the amplification control signal andgenerating an output signal with compression and reduced noise. Theapparatus further comprises an auxiliary noise reduction unit connectedto the input for generating an auxiliary noise reduced signal, thecompression control signal being the auxiliary noise reduced signal.

The apparatus may further comprise a main noise reduction unit connectedto the input for generating a noise reduced signal and supplying thenoise reduced signal in place of the input signal to the amplificationunit.

Preferably, the input signal contains speech and the main noisereduction unit comprises a detector connected to the input and providinga detection signal indicative of the presence of speech and a magnitudemeans for determining the magnitude spectrum of the input signal(|X(f)|), with both the detector and the magnitude means being connectedto the input of the apparatus. The main noise reduction unit furthercomprises a spectral estimate means for generating a noise magnitudespectral estimate (|{circumflex over (N)}(f)|) and being connected tothe detector and to the input of the apparatus, a noise filtercalculation unit connected to the spectral estimate means and themagnitude means, for receiving the noise magnitude spectral estimate(|{circumflex over (N)}(f)|) and magnitude spectrum of the input signal(|X(f)|) and calculating an attenuation function (H(f)), and amultiplication unit coupled to the noise filter calculation unit and theinput signal for producing the noise reduced-signal.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

For a better understanding of the present invention and to show moreclearly how it may be carried into effect, reference will now be made,by way of example, to the accompanying drawings in which:

FIG. 1 is a conceptual blocked diagram for hearing aid noise reductionand compression;

FIG. 2 shows a detailed blocked diagram for noise reduction in a hearingaid;

FIG. 3 shows a modified auto-correlation scheme performed in segments.

DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring first to FIG. 1, there is shown schematically a basic strategyemployed by the present invention. An input 10 for a noisy signal issplit into two paths 12 and 14. In the upper path 12, the noisereduction is effected as indicated in block 16. In the lower path 14,noise reduction is effected in unit 18. The noise reduction unit 18provides a cleaner signal that is supplied to compression circuitry 20,and the compression circuitry controls amplification unit 22 amplifyingthe signal in the upper path to generate an output signal at 24.

Here, the position of the noise reduction unit 18 can advantageouslyprovide a cleaner signal for controlling the compression stage. Thenoise reduction unit 18 provides a first generating means whichgenerates an auxiliary signal from an auxiliary noise reductionalgorithm. The auxiliary algorithm performed by unit 18 may be identicalto the one performed by unit 16, except with different parameters. Sincethe auxiliary noise reduced signal is not heard, unit 18 can reducenoise with increased aggression. This auxiliary signal, in turn,controls the compression circuitry 20, which comprises second generatingmeans for generating a control input for controlling the amplificationunit 22.

The noise reduction unit 16 is optional, and can be effected by using adifferent noise reduction algorithm from that in the noise reductionunit 18. If the same algorithm is used for both noise reductionprocesses 16 and 18, then the two paths can be merged prior to beingsplit up to go to units 20 and 22. As noted, the noise reduction in theupper path may be altered or eliminated.

With reference to FIG. 2, this shows a block diagram of a specificrealization of the proposed noise reduction technique which ispreferably carried out by noise reduction unit 18 (and possibly alsonoise reduction unit 16). The incoming signal at 10 is first blocked andwindowed, as detailed in applicant's simultaneously filed internationalapplication Ser. No. PCT/CA98/00329 corresponding to internationalpublication no. WO 98/47313 which is incorporated herein by reference.The blocked and windowed output provides the input to the frequencytransform (all of these steps take place, as indicated, at 32), whichpreferably here is a Discrete Fourier Transform (DFT),to provide asignal X(f). The present invention is not however restricted to a DFTand other transforms can be used. A known, fast way of implementing aDFT with mild restrictions on the transform size is the Fast FourierTransform (FFT). The input 10 is also connected to a speech detector 34which works in parallel to isolate the pauses in the incoming speech.For simplicity, reference is made here to “speech”, but it will beunderstood that this encompasses any desired audio signal, capable ofbeing isolated or detected by detector 34. These pauses provideopportunities to update the noise spectral estimate. This estimate isupdated only during speech pauses as a running slow average. When speechis detected, the noise estimate is frozen.

As indicated at 38, the outputs from both the unit 32 and the voicedetection unit 34 are connected to block 38 which detects the magnitudespectrum of the incoming noise, |{circumflex over (N)}(f)|. Themagnitude spectrum detected by unit 38 is an estimate. The output ofunit 32 is also connected to block 36 for detecting the magnitudespectrum of the incoming noisy signal, |X(f)|.

A noise filter calculation 40 is made based on |X(f)| and |{circumflexover (N)}(f)|, to calculate an attenuation function H(f). As indicatedat 42, this is used to control the original noisy signal X(f) bymultiplying X(f) by H(f). This signal is subject to an inverse transformand overlap-add resynthesis in known manner at 44, to provide a noisereduced signal 46. The noise reduced signal 46 in FIG. 2 may correspondto either of the signal at 12 or 14 in FIG. 1.

During speech utterances, the magnitude spectrum is compared with thenoise spectral estimate. In general, frequency dependent attenuation iscalculated as a function of the two input spectra. Frequency regionswhere the incoming signal is higher than the noise are attenuated lessthan regions where the incoming signal is comparable or less than thenoise. The attenuation function is generally given by${H(f)} = \left\lbrack \frac{{{S(f)}}^{2}}{{{S(f)}}^{2} + {{N(f)}}^{2}} \right\rbrack^{\alpha}$

-   -   where H(f) is the attenuation as a function of frequency    -   S(f) is the clean speech spectrum    -   N(f) is the noise spectrum    -   α is the attenuation rule        The attenuation rule preferably selected is the Wiener        attenuation rule which corresponds to α equal to 1. The Wiener        rule minimizes the noise power relative to the speech. Other        attenuation rules can also be used, for example the spectral        subtraction rule having α equal to 0.5.

Since neither S(f) nor N(f) are precisely known and wold require apriori knowledge of the clean speech and noise spectra, they arereplaced by estimates Ŝ(f) and {circumflex over (N)}(f):|Ŝ(f)|² −|{circumflex over (N)}(f)|²where X(f) is the incoming speech spectrum and {circumflex over (N)}(f)is the noise spectrum as estimated during speech pauses. Given perfectestimates of the speech and noise spectra, application of this formulayields the optimum (largest) signal-to-noise-ratio (SNR). Although theSNR would be maximized using this formula, the noise in the resultingspeech is still judged as excessive by subjective assessment. Animproved implementation of the formula taking into account theseperceptual aspects is given by:${H(f)} = \left\lbrack \frac{{{X(f)}}^{2} - {\beta{{\hat{N}(f)}}^{2}}}{{{X(f)}}^{2}} \right\rbrack^{\alpha}$

-   -   where: β is an oversubtraction factor    -   α is the attenuation rule    -   H(f) should be between 0.0 and 1.0 to be meaningful. When        negative results are obtained, H(f) simply set to zero at that        frequency. In addition, it is beneficial to increase the minimum        value of H(f) somewhat above zero to avoid complete suppression        of the noise. While counter-intuitive, this reduces the musical        noise artifact (discussed later) to some extent. The parameter α        governs the attenuation rule for increasing noise levels.        Generally, the higher α is set, the more the noise is punished        as X(f) drops. It was found that the best perceptual results are        obtained with α=1.0. The special case of α=1.0 and β=1.0        corresponds to power spectrum subtraction yielding the Wiener        filter solution as described above.

the parameter β controls the amount of additional noise suppressionrequired; it is ideally a function of the input noise level. Empiricallyit was noticed that under very light noise (SNR>40 dB) β should be zero.For lower SNR signals, the noise reduction become less reliable and isgradually turned off. An example of this additional noise reduction is:$\begin{matrix}{\beta = 0} & {{{for}{\mspace{11mu}\;}{SNR}} < 0} \\{\beta = {\beta_{0}\frac{SNR}{5}}} & {{{for}\mspace{14mu} 0} < {SNR} < 5} \\{\beta = {\beta_{0}\left\lbrack {1 - \frac{\left( {{SNR} - 5} \right)}{35}} \right\rbrack}} & {{{for}\mspace{14mu} 5} < {SNR} < 40} \\{\beta = 0} & {{{for}\mspace{14mu}{SNR}} > 40}\end{matrix}$In this example, β₀ refers to the maximum attenuation, 5.0. In effect,from SNR=0, the attenuation β is ramped up uniformly to a maximum, β₀,at SNR=5, and this is then uniformly ramped down to zero at SNR=40.

Another aspect of the present invention provides improvements inperceptual quality making β a function of frequency. As an instance ofthe use of this feature, it was found that to avoid excessiveattenuation of high frequency information, it was necessary to apply apreemphasis function, P(f), to the input spectrum X(f), where P(f) is anincreasing function of frequency. The effect of this preemphasisfunction is to artificially raise the input spectrum above the noisefloor at high frequencies. The attenuation rule will then leave thehigher frequencies relatively intact. This preemphasis is convenientlyaccomplished by reducing β at high frequencies by the preemphasisfactor. ${\hat{\beta}(f)} = \frac{\beta}{{P(\int)}^{\prime}}$where {circumflex over (β)} is β after preemphasis.

Without further modification, the above formula can yield noise reducedspeech with an audible artifact known as musical noise. This occurs,because in order for the noise reduction to be effective in reducingnoise, the frequency attenuation function has to be adaptive. The veryact of adapting this filter allows isolated frequency regions of low SNRto flicker in and out of audibility leading to this musical noiseartifact. Various methods are used to reduce this problem. Slowing downthe adaptation rate significantly reduces this problem. In this method,a forgetting factor, γ is introduced to slow abrupt gain changes in theattenuation function:G _(n)(f)=(1−γ)I I(f)+γG _(n-1)(f)where G_(n)(f) and G_(n-1)(f) are the smoothed attenuation functions atthe n'th and (n−1)'th time frames.

Further improvements in perceptual quality are possible by making β (inaddition to being a function of frequency) a function of perceptualdistortion. In this method, the smoothing function (instead of a simpleexponential or forgetting factor as above) bases its decision onadapting G_(n)(f) on whether such a change is masked perceptually. Theperceptual adaptation algorithm uses the ideal attenuation functionII(f) as a target because it represents the best SNR attainable. Thealgorithm decides how much G_(n)(f) can be adjusted while minimizing theperceptual distortion. The decision is based on a number of maskingcriteria in the output spectrum including:

1. Spread of masking—changes in higher frequency energy are masked bythe presence of energy in frequencies in the vicinity—especially lowerfrequencies;

2. Previous energy—changes in louder frequency components are moreaudible that changes in weaker frequency components;

-   -   3. Threshold of hearing—there is no point in reducing the noise        significantly below the threshold of hearing at a particular        frequency;

4. Previous attenuation—low levels should not be allowed to jump uprapidly—high levels should not suddenly drop rapidly unless masked by1), 2) or 3).

For applications where the noise reduction is used to preprocess theinput signal before reaching the compression circuitry (schematicallyshown in FIG. 1), the perceptual characteristics of the noise reducedsignal are less important. In fact, it may prove advantageous to performthe noise reduction with two different suppression algorithms asmentioned above. The noise reduction 16 would be optimized forperceptual quality while the other noise reduction 18 would be optimizedfor good compression performance.

A key element to the success of the present noise suppression orreduction system is the speech or voicing detector. It is crucial toobtain accurate estimates of the noise spectrum. If the noise spectralestimate is updated during periods of speech activity, the noisespectrum will be contaminated with speech resulting in speechcancellation. Speech detection is very difficult, especially under heavynoise situations. Although, a three-way distinction between voicedspeech, unvoiced speech (consonants) and noise is possible under lightnoise conditions, it was found that the only reliable distinctionavailable in heavy noise was between voiced speech and noise. Given theslow averaging of the noise spectrum, the addition of low-energyconsonants is insignificant.

Thus, another aspect of the present invention uses an auto-correlationfunction to detect speech, as the advantage of this function is therelative ease with which a periodic signal is detected. As will beappreciated by those skilled in the art, an inherent property of theauto-correlation function of a periodic signal is that it shows a peakat the time lag corresponding to the repetition Period (see Rabiner, L,R., and Schafer, R. W., Digital Processing of Speech Signals, (PrenticeHall Inc., 1978) which is incorporated herein by reference). Sincevoiced speech is nearly periodic in time at the rate of its pitchperiod, a voicing detector based on the auto-correlation function wasdeveloped. Given a sufficiently long auto-correlation, the uncorrelatednoise tends to cancel out as successive pitch periods are averagedtogether.

A strict short-time auto-correlation requires that the signal first beblocked to limit the time extent (samples outside the block are set tozero). This operation is followed by an auto-correlation on the block.The disadvantage of this approach is that the auto-correlation functionincludes fewer samples as the time lag increases. Since the pitch lag(typically between 40 and 240 samples (equivalent to 2.5 to 15milliseconds) is a significant portion of the auto-correlation frame(typically 512 samples or 32 milliseconds), a modified version of theauto-correlation function avoiding this problem was calculated. Thismodified version of the auto-correlation function is described inRabiner, L. R., and Schafer, R. W., Digital Processing of SpeechSignals, supra. In this method, the signal is blocked and correlatedwith a delayed block (of the same length) of the signal. Since thesamples in the delayed block include samples not present in the firstblock, this function is not a strict auto-correlation but showsperiodicities better.

It is realized that a hearing aid is a real-time system and that allcomputational elements for each speech block are to be completed beforethe next arrives. The calculation time of a long auto-correlation, whichis required only every few speech blocks, would certainly bring thesystem to a halt every time it must be calculated. It is thereforerecognized that the auto-correlation should be segmented into a numberof shorter sections which can be calculated for each block and stored ina partial correlation table. The complete auto-correlation is determinedby stacking these partial correlations on top of each other and addingas shown in FIG. 3.

Referring to FIG. 3, input sample 50 is divided into separate blocksstored in memory buffers as indicated at 52. The correlation buffers 52are connected to a block correlation unit 54, where the auto-correlationis performed. Partial cross-correlations 56 are summed to give the finalcorrelation 58.

This technique quickly yields the exact modified auto-correlation and isthe preferred embodiment when sufficient memory is available to storethe partial correlations.

When memory space considerations rule out the above technique, a form ofexponential averaging may be used to reduce the number of correlationbuffers to a single buffer. In this technique, successive partialcorrelations are summed to the scaled down previous contents of thecorrelation buffer. This simplification significantly reduces the memorybut implicitly applies an exponential window to the input sequence. Thewindowing action, unfortunately, reduces time periodicities. The effectis to spread the autocorrelation peak to a number of adjacent time lagsin either direction. This peak smearing reduces the accuracy of thevoicing detection somewhat.

In the implementations using an FFT transform block, these partialcorrelations (for either technique given above) can be performed quicklyin the frequency domain. For each block, the correlation operation isreduced to a sequence of complex multiplications on the transformed timesequences. The resulting frequency domain sequences can be addeddirectly together and transformed back to the time domain to provide thecomplete long auto-correlation. In an alternate embodiment, thefrequency domain correlation results are never inverted back to the timedomain. In this realization, the pitch frequency is determined directlyin the frequency domain.

Since the auto-correlation frame is long compared to the (shorter)speech frame, the voicing detection is delayed compared to the currentframe. This compensation for this delay is accomplished in the noisespectrum update block.

An inter-frame constraint was placed on frames considered as potentialcandidates for speech pauses to further reduce false detection of noiseframes. The spectral distance between the proposed frame and theprevious estimates of the noise spectrum are compared. Large valuesreduce the likelihood that the frame is truly a pause. The voicingdetector takes this information, the presence or absence of anauto-correlation peak, the frame energy, and a running average of thenoise as inputs.

1. A method of reducing noise in an input signal, said input signalcontaining speech and noise related to each other by a signal to noiseratio, the method comprising the steps: (1) detecting the presence andabsence of speech; (2) in the absence of speech, determining a noisemagnitude spectral estimate (|{circumflex over (N)}(f)|); (3) in thepresence of speech, comparing the magnitude spectrum of the input signal(|X(f)|) to the noise magnitude spectral estimate (|{circumflex over(N)}(f)|); (4) calculating an attenuation function (H(f)) from themagnitude spectrum of the input signal (|X(f)|) and the noise magnitudespectral estimate (|{circumflex over (N)}(f)|), the attenuation function(H(f)) being dependent on the signal to noise ratio; and, (5) modifyingthe input signal by the attenuation function (H(f)) to generate a noisereduced signal wherein there is no substantial modification to the inputsignal for very low and for very high signal to noise ratios.
 2. Amethod as claimed in claim 1, further comprising the steps of: (6)supplying the input signal to an amplification unit; (7) providing thenoise reduced signal to a compression circuit which generates a controlsignal for the amplification unit; and (8) controlling the amplificationunit with the control signal to modify the input signal to generate anoutput signal with compression and reduced noise.
 3. A method as claimedin claim 2, wherein step (6) comprises subjecting the input signal to amain noise reduction algorithm to generate a main noise reduced signaland providing the main noise reduced signal to the amplification unit.4. A method as claimed in claim 3, wherein the main noise reductionalgorithm comprises the method of claim
 1. 5. A method as claimed inclaim 3, wherein the main noise reduction algorithm is different fromthe method of claim
 1. 6. A method as claimed in claim 2, wherein step(6) comprises applying steps (1) to (5) to the input signal prior tosupplying the input signal to the amplification unit.
 7. A method asclaimed in claim 1, wherein the square of the speech magnitude spectralestimate (|Ŝ(f)|) is determined by subtracting the square of the noisemagnitude spectral estimate (|{circumflex over (N)}(f)|) from the squareof the magnitude spectrum of the input signal (|X(f)|).
 8. A method asclaimed in claim 7, wherein the attenuation function is calculated inaccordance with the following equation:${H(f)} = \left\lbrack \frac{{{X(f)}}^{2} - {\beta{{\hat{N}(f)}}^{2}}}{{{X(f)}}^{2}} \right\rbrack^{\alpha}$where H(f) is the attenuation function, |X(f)| is the magnitude spectrumof the input signal; |{circumflex over (N)}(f)| is the noise magnitudespectral estimate, β is an oversubtraction factor and α is anattenuation rule, wherein α and β are selected to give a desiredattenuation function.
 9. A method as claimed in claim 8, wherein theoversubtraction factor β is varied as a function of the signal to noiseratio, with β being zero for high and low signal to noise ratios andwith β being increased as the signal to noise ratio increases above zeroto a maximum value at a predetermined signal to noise ratio and forhigher signal to noise ratios β decreases to zero at a secondpredetermined signal to noise ratio greater than the first predeterminedsignal to noise ratio.
 10. A method as claimed in claim 9, wherein theoversubtraction factor β is divided by a preemphasis function P(f) togive a modified oversubtraction factor {circumflex over (β)}(f), thepreemphasis function being such as to reduce {circumflex over (β)}(f) athigh frequencies, and thereby reduce attenuation at high frequencies.11. A method as claimed in claim 8, wherein the rate of change of theattenuation function (H(f)) is controlled to prevent abrupt and rapidchanges in the attenuation function (H(f)).
 12. A method as claimed inclaim 8, wherein the attenuation function (H(f)) is calculated atsuccessive time frames, and the attenuation function (H(f)) iscalculated in accordance with the following equation:G _(n)(f)=(1=γ)H(f)+γG _(n-1)(f) wherein G_(n)(f) and G_(n-1)(f) are thesmoothed attenuation functions at the n'th and (n−1) 'th time frames,and γ is a forgetting factor.
 13. A method as claimed in claim 12,wherein β is a function of perceptual distortion.
 14. A method asclaimed in claim 1 which includes remotely turning noise suppression onand off.
 15. A method as claimed in claim 1 which includes automaticallydisabling noise reduction in the presence of very light noise orextremely adverse environments.
 16. A method as claimed in claim 1 whichincludes detecting speech with a modified auto-correlation function. 17.A method as claimed in claim 16, wherein the auto-correlation functioncomprises: (1) taking an input sample and separating it into shortblocks and storing the blocks in correlation buffers; (2) correlatingthe blocks with one another, to form partial correlations; and (3)summing the partial correlations to obtain a final correlation.
 18. Amethod as claimed in claim 17, wherein the method is carried out bydigital signal processing and wherein the method includes using a FastFourier Transform to generate the partial correlations and includesdetection of voiced speech directly in the frequency domain.
 19. Amethod as claimed in claim 1, wherein detecting the presence or absenceof speech comprises: (1) taking a block of the input signal andperforming an auto-correlation on that block to form a correlatedsignal; and, (2) checking the correlated signal for the presence of aperiodic signal having a pitch corresponding to that for a desired audiosignal.
 20. A method as claimed in claim 19, wherein theauto-correlation is performed on a first block taken from the inputsignal, and a delayed block from the audio signal.
 21. A method asclaimed in claim 20, wherein each block is subdivided into a pluralityof shorter sections and the correlation comprises a correlation betweenpairs of the shorter sections to form partial correlations, andsubsequently summing the partial correlations to obtain the correlatedsignal.
 22. A method as claimed in claim 21, wherein an input signal isstored as a plurality of samples in a pair of correlation buffers, andthe auto-correlation is performed on the signals in the buffers todetermine the partial correlations, which partial correlations aresummed and stored.
 23. An apparatus, for reducing noise in a singleinput signal, the apparatus including an input for receiving the singleinput signal, the apparatus comprising: (a) a compression circuit forreceiving a compression control signal and generating an amplificationcontrol signal in response; (b) an amplification unit for receiving aninput amplification signal and the amplification control signal andgenerating an output signal with compression and reduced noise under thecontrol of the amplification control signal; (c) an auxiliary noisereduction unit connected to the input for generating an auxiliary noisereduced signal, the compression control signal being the auxiliary noisereduced signal; and, (d) a main noise reduction unit connected to theinput and the amplification unit for receiving the single input signaland generating a noise reduced signal, the noise reduced signal beingthe input amplification signal; wherein, the single input signalcontains speech and noise related to each other by a signal to noiseratio and the main noise reduction unit generates the noise reducedsignal in dependence upon the signal to noise ratio, wherein there is nosubstantial modification to the single input signal for very low and forvery high signal to noise ratios.
 24. An apparatus as claimed in claim23, wherein the input signal contains speech and the main noisereduction unit comprises: (1) a detector connected to said input andproviding a detection signal indicative of the presence of speech; (2)magnitude means for determining the magnitude spectrum of the inputsignal (|X(f)|), with both the detector and the magnitude means beingconnected to the input of the apparatus; (3) spectral estimate means forgenerating a noise magnitude spectral estimate (|{circumflex over(N)}(f)|) and being connected to the detector and to the input of theapparatus; (4) a noise filter calculation unit connected to the spectralestimate means and the magnitude means, for receiving the noisemagnitude spectral estimate (|{circumflex over (N)}(f)|) and magnitudespectrum of the input signal (|X(f)|) and calculating an attenuationfunction (H(f)); and, (5) a multiplication unit coupled to the noisefilter calculation unit and the input signal for producing the noisereduced signal.
 25. An apparatus as claimed in claim 24, which includesa frequency transform means connected between said input and both of themagnitude means and the spectral estimate means for transforming thesignal into the frequency domain to provide a transformed signal (X(f))wherein the magnitude means determines the magnitude spectrum (|X(f)|)from the transformed signal (X(f)), and wherein the spectral estimatemeans determines the noise spectral estimate (|{circumflex over(N)}(f)|) from the transformed signal (X(f)) in the absence of speech,the apparatus further including inverse frequency transform means forreceiving a transformed noise reduced signal from the multiplicationunit, the inverse frequency transform means providing the noise reducedsignal.
 26. An apparatus as claimed in claim 25, wherein the noisefilter calculation unit determines the square of the speech magnitudespectral estimate by subtracting the square of the noise magnitudespectral estimate from the square of the magnitude spectrum of the inputsignal and wherein the noise filter calculation unit calculates theattenuation function (H(f)), as a function of frequency, in accordancewith the following equation:${H(f)} = \left\lbrack \frac{{{X(f)}}^{2} - {\beta{{\hat{N}(f)}}^{2}}}{{{X(f)}}^{2}} \right\rbrack^{\alpha}$where f denotes frequency, H(f) is the attenuation function, |X(f)| isthe magnitude spectrum of the input audio signal; |{circumflex over(N)}(f)| is the noise magnitude spectral estimate, β is anoversubtraction factor and α is an attenuation rule, wherein α and β areselected to give a desired attenuation function.
 27. An apparatus asclaimed in claim 23, wherein the main noise reduction unit and theauxiliary noise reduction unit employ the same noise reductionalgorithm.
 28. An apparatus as claimed in claim 23, wherein theauxiliary noise reduction unit is different from the main noisereduction unit.
 29. A method of reducing noise in an input signal, saidinput signal containing speech and noise related to each other by asignal to noise ratio, the method comprising the steps: (1) detectingthe presence and absence of speech; (2) in the absence of speech,determining a noise magnitude spectral estimate (|{circumflex over(N)}(f)|); (3) in the presence of speech, comparing the magnitudespectrum of the input signal (|X(f)|) to the noise magnitude spectralestimate (|{circumflex over (N)}(f)|); (4) calculating an attenuationfunction (H(f)) from the magnitude spectrum of the input signal (|X(f)|)and the noise magnitude spectral estimate (|{circumflex over (N)}(f)|),the attenuation function (H(f)) being dependent on the signal to noiseratio; and, (5) modifying the input signal by the attenuation function(H(f)) to generate a noise reduced signal wherein there is nosubstantial modification to the input signal for very low and for veryhigh signal to noise ratios and wherein the amount of attenuationprovided by the attenuation function is increased as the signal to noiseratio increases above zero to a maximum value at a predetermined signalto noise ratio and for higher signal to noise ratios the amount ofattenuation provided by the attenuation function decreases to zero at asecond predetermined signal to noise ratio greater than the firstpredetermined signal to noise ratio.
 30. An apparatus, for reducingnoise in an input signal containing speech and noise related to eachother by a signal to noise ratio, the apparatus including an input forreceiving the input signal, the apparatus comprising: (a) a compressioncircuit for receiving a compression control signal and generating anamplification control signal in response; (b) an amplification unit forreceiving an input amplification signal and the amplification controlsignal and generating an output signal with compression and reducednoise; and, (c) an auxiliary noise reduction unit connected to the inputfor generating an auxiliary noise reduced signal, the compressioncontrol signal being the auxiliary noise reduced signal, wherein theauxiliary noise reduction unit generates the auxiliary noise reducedsignal in dependence upon the signal to noise ratio, wherein there is nosubstantial modification to the input signal for very low and for veryhigh signal to noise ratios.
 31. An apparatus, for reducing noise in aninput signal containing speech and noise related to each other by asignal to noise ratio, the apparatus including an input for receivingthe input signal, the apparatus comprising: (a) a compression circuitfor receiving a compression control signal and generating anamplification control signal in response; (b) an amplification unit forreceiving the input signal and the amplification control signal andgenerating an output signal with compression and reduced noise; and, (c)an auxiliary noise reduction unit connected to the input for generatingan auxiliary noise reduced signal, the compression control signal beingthe auxiliary noise reduced signal, wherein the auxiliary noisereduction unit generates the auxiliary noise reduced signal according toan attenuation function in dependence upon the signal to noise ratio,wherein there is no substantial modification to the input signal forvery low and for very high signal to noise ratios and wherein the amountof attenuation provided by the attenuation function is increased as thesignal to noise ratio increases above zero to a maximum value at apredetermined signal to noise ratio and for higher signal to noiseratios the amount of attenuation provided by the attenuation functiondecreases to zero at a second predetermined signal to noise ratiogreater than the first predetermined signal to noise ratio.
 32. Anapparatus, for reducing noise in an input signal, the apparatusincluding an input for receiving the input signal, the apparatuscomprising: (a) a compression circuit for receiving a compressioncontrol signal and generating an amplification control signal inresponse; (b) an amplification unit for receiving an input amplificationsignal and the amplification control signal and generating an outputsignal with compression and reduced noise under the control of theamplification control signal; (c) an auxiliary noise reduction unitconnected to the input for generating an auxiliary noise reduced signal,the compression control signal being the auxiliary noise reduced signal;and, (d) a main noise reduction unit connected to the input and theamplification unit for receiving the input signal and generating a noisereduced signal, the input amplification signal being the noise reducedsignal; wherein, the main noise reduction unit employs a first noisereduction algorithm and the auxiliary noise reduction unit employs asecond noise reduction algorithm, the second noise reduction algorithmbeing adapted to attack noise more aggressively than the first noisereduction algorithm.